Medical image segmentation is of critical importance in the domain of contemporary medical imaging. However, U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information. Although the subsequent U-KAN model enhances nonlinear representation capabilities, it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling, resulting in insufficient segmentation accuracy for edge structures and minute lesions. To address these challenges, this paper proposes the RE-UKAN model, which innovatively improves upon U-KAN. Firstly, a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings, thus enhancing modelling capabilities for complex pathological structures. Secondly, Efficient Local Attention (ELA) is integrated to suppress spatial detail loss during downsampling, thereby improving the perception of edge structures and minute lesions. Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics, with particularly outstanding performance on the TN-SCUI 2020 dataset, achieving IoU of 88.18% and Dice of 93.57%. Compared to the baseline model, it achieves improvements of 3.05% and 1.72%, respectively. These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks, providing a reliable solution for clinical precision segmentation.