diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst
index 749d75ef82d40..47ccdad59de3c 100644
--- a/doc/source/whatsnew/v2.0.0.rst
+++ b/doc/source/whatsnew/v2.0.0.rst
@@ -872,6 +872,7 @@ Performance improvements
 - Performance improvement in :class:`Period` constructor when constructing from a string or integer (:issue:`38312`)
 - Performance improvement in :func:`to_datetime` when using ``'%Y%m%d'`` format (:issue:`17410`)
 - Performance improvement in :func:`to_datetime` when format is given or can be inferred (:issue:`50465`)
+- Performance improvement in :meth:`Series.median` for nullable dtypes (:issue:`50838`)
 - Performance improvement in :func:`read_csv` when passing :func:`to_datetime` lambda-function to ``date_parser`` and inputs have mixed timezone offsetes (:issue:`35296`)
 - Performance improvement in :func:`isna` and :func:`isnull` (:issue:`50658`)
 - Performance improvement in :meth:`.SeriesGroupBy.value_counts` with categorical dtype (:issue:`46202`)
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 02372356d3fe4..61f1bcdef9568 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -746,16 +746,19 @@ def nanmedian(values, *, axis: AxisInt | None = None, skipna: bool = True, mask=
     2.0
     """
 
-    def get_median(x):
-        mask = notna(x)
-        if not skipna and not mask.all():
+    def get_median(x, _mask=None):
+        if _mask is None:
+            _mask = notna(x)
+        else:
+            _mask = ~_mask
+        if not skipna and not _mask.all():
             return np.nan
         with warnings.catch_warnings():
             # Suppress RuntimeWarning about All-NaN slice
             warnings.filterwarnings(
                 "ignore", "All-NaN slice encountered", RuntimeWarning
             )
-            res = np.nanmedian(x[mask])
+            res = np.nanmedian(x[_mask])
         return res
 
     values, mask, dtype, _, _ = _get_values(values, skipna, mask=mask)
@@ -796,7 +799,7 @@ def get_median(x):
 
     else:
         # otherwise return a scalar value
-        res = get_median(values) if notempty else np.nan
+        res = get_median(values, mask) if notempty else np.nan
     return _wrap_results(res, dtype)