Histopathological image segmentation suffers from severe domain shifts caused by staining variation, imaging conditions, and tissue diversity across sites and organs. Shallow-Deep Synergy improves generalization in U-Net-based segmentation by explicitly combining the complementary strengths of shallow fine-detail features and deep semantic features. This design strengthens dense prediction performance under cross-domain settings where standard domain generalization methods are less effective.