What deep learning strategies best balance accuracy and interpretability in medical image segmentation for disease progression analysis?

Medical imaging (CT/MRI) segmentation is vital for tracking disease, but black-box models reduce clinical trust. Methods like explainable AI (XAI), uncertainty quantification, and hybrid modeling may bridge this gap. What approaches are most promising?