diff --git a/doc/source/whatsnew/v1.4.0.rst b/doc/source/whatsnew/v1.4.0.rst
index 8732e1c397ce5..d1e209adb1b8f 100644
--- a/doc/source/whatsnew/v1.4.0.rst
+++ b/doc/source/whatsnew/v1.4.0.rst
@@ -623,6 +623,8 @@ Reshaping
 - Bug in :func:`crosstab` would fail when inputs are lists or tuples (:issue:`44076`)
 - Bug in :meth:`DataFrame.append` failing to retain ``index.name`` when appending a list of :class:`Series` objects (:issue:`44109`)
 - Fixed metadata propagation in :meth:`Dataframe.apply` method, consequently fixing the same issue for :meth:`Dataframe.transform`, :meth:`Dataframe.nunique` and :meth:`Dataframe.mode` (:issue:`28283`)
+- Bug in :meth:`DataFrame.stack` with ``ExtensionDtype`` columns incorrectly raising (:issue:`43561`)
+-
 
 Sparse
 ^^^^^^
diff --git a/pandas/core/reshape/reshape.py b/pandas/core/reshape/reshape.py
index 9c7107ab40644..6c6b14653df75 100644
--- a/pandas/core/reshape/reshape.py
+++ b/pandas/core/reshape/reshape.py
@@ -745,13 +745,15 @@ def _convert_level_number(level_num, columns):
             if frame._is_homogeneous_type and is_extension_array_dtype(
                 frame.dtypes.iloc[0]
             ):
+                # TODO(EA2D): won't need special case, can go through .values
+                #  paths below (might change to ._values)
                 dtype = this[this.columns[loc]].dtypes.iloc[0]
                 subset = this[this.columns[loc]]
 
                 value_slice = dtype.construct_array_type()._concat_same_type(
                     [x._values for _, x in subset.items()]
                 )
-                N, K = this.shape
+                N, K = subset.shape
                 idx = np.arange(N * K).reshape(K, N).T.ravel()
                 value_slice = value_slice.take(idx)
 
diff --git a/pandas/tests/frame/test_stack_unstack.py b/pandas/tests/frame/test_stack_unstack.py
index 404baecdfecac..62512249dabfc 100644
--- a/pandas/tests/frame/test_stack_unstack.py
+++ b/pandas/tests/frame/test_stack_unstack.py
@@ -2099,3 +2099,27 @@ def test_stack_unsorted(self):
         result = DF.stack(["VAR", "TYP"]).sort_index()
         expected = DF.sort_index(axis=1).stack(["VAR", "TYP"]).sort_index()
         tm.assert_series_equal(result, expected)
+
+    def test_stack_nullable_dtype(self):
+        # GH#43561
+        columns = MultiIndex.from_product(
+            [["54511", "54515"], ["r", "t_mean"]], names=["station", "element"]
+        )
+        index = Index([1, 2, 3], name="time")
+
+        arr = np.array([[50, 226, 10, 215], [10, 215, 9, 220], [305, 232, 111, 220]])
+        df = DataFrame(arr, columns=columns, index=index, dtype=pd.Int64Dtype())
+
+        result = df.stack("station")
+
+        expected = df.astype(np.int64).stack("station").astype(pd.Int64Dtype())
+        tm.assert_frame_equal(result, expected)
+
+        # non-homogeneous case
+        df[df.columns[0]] = df[df.columns[0]].astype(pd.Float64Dtype())
+        result = df.stack("station")
+
+        # TODO(EA2D): we get object dtype because DataFrame.values can't
+        #  be an EA
+        expected = df.astype(object).stack("station")
+        tm.assert_frame_equal(result, expected)