Owing to the limitations of convolutional operations， existing skin lesion image segmentation networks are unable to model the global contextual information in images， resulting in their inability to effectively capture the target structural information of images. In this paper， a U-shaped hybrid network with cross-self-attention coding was designed for skin lesion image segmentation. Firstly， the designed multi-head gated position cross self-attention encoder was introduced in the last two layers of the U-shaped network to enable it to learn the long-term dependencies of semantic information in images and to compensate for the lack of global modelling capability of the convolutional operation； Secondly， a novel position channel attention mechanism was implemented in the skip connection part to encode the channel information of the fused features and retain the positional information to improve the network's ability to capture the target structure； finally， a regularised dice loss function was designed to enable the network to trade off between false positives and false negatives to improve the network's segmentation results. Experimental results on ISBI2017 and ISIC2018 datasets show that the network presented in this paper achieves Dice score of 91.48% and 91.30%， and IoU of 84.42% and 84.12%， respectively. The network outperforms other networks in terms of segmentation accuracy with fewer parameters and lower computational complexity. Therefore， it can efficiently segment the target region of skin lesion images and aid in the adjunctive diagnosis of skin diseases.