Feedback‐Driven Medical Image Segmentation: Modelling on Boundary‐Sensitive Feature Preservation

Accurate segmentation of medical images, particularly along ambiguous lesion boundaries, plays a vital role in clinical diagnosis and treatment planning. Conventional segmentation approaches often struggle to recover fine‐grained contours due to spatial detail loss caused by convolutional downsampling and the inherent sparsity of edge features, leading to incomplete or imprecise delineations. To address these limitations, we propose a feedback‐driven uncertainty‐aware framework, termed UFU‐Net, which iteratively quantifies prediction uncertainty and progressively refines features in low‐confidence regions. Specifically, (i) an uncertainty module interprets the weak decoder's outputs to produce heatmaps that explicitly direct attention to ambiguous areas; (ii) a bidirectional feature‐fusion module aggregates multi‐scale uncertainty cues during decoding to strengthen edge representations; and (iii) a local Manhattan self‐attention mechanism builds pixel‐level context in contour regions, enabling sub‐pixel detail reconstruction. Experiments on ISIC2018 , BUSI and Glas show consistent improvements over state‐of‐the‐art methods, and qualitative results confirm accurate localisation and refinement of irregular lesion contours.