To address the challenge of simultaneously achieving accurate segmentation and computational efficiency in skin lesion segmentation, this paper proposes a boundary-enhanced sparse attention network (BES-UNet). The network achieves high-precision segmentation and efficient inference of skin lesions, especially those with fuzzy boundaries and irregular shapes, through the following two key innovations: (1) a bidirectional enhanced adaptive learning framework is proposed, which achieves the collaborative optimization of accurate boundary positioning and overall regional segmentation; (2) designs a boundary-aware dynamic sparse attention mechanism, which uses a content-adaptive dynamic sparsification strategy and a multi-scale shared memory space to significantly reduce model computational complexity while improving the ability to identify lesion edges. Experiments on the ISIC2017 and ISIC2018 public datasets show that BES-UNet has a strong advantage in segmenting fuzzy or irregular boundary regions, with an average intersection over union (mIoU) of up to 81.98% and a Dice similarity coefficient (DSC) of 89.65%. The F1 score for boundary regions is as high as 60.17%, an improvement of 1.05–2.22 percentage points over existing methods. Ablation experiments further confirmed the effectiveness of each component, providing a feasible solution for high-precision skin lesion segmentation in resource-constrained environments.