In Hematoxylin-Eosin （HE）-stained pathological images， the uneven distribution of cell staining and the diversity of various tissue morphologies bring great challenges to automated segmentation. Traditional convolutions cannot capture the correlation features between pixels in a large neighborhood， making it difficult to further improve the segmentation performance. Therefore， a Multi-Channel Segmentation Network with gated axial self-attention （MCSegNet） model was proposed to achieve accurate segmentation of nuclei in pathological images. In the proposed model， a dual-encoder and decoder structure was adopted， in which the axial self-attention encoding channel was used to capture global features， while the convolutional encoding channel based on residual structure was used to obtain local fine features. The feature representation was enhanced by feature fusion at the end of the encoding channel， providing a good information base for the decoder. And in the decoder， segmentation results were gradually generated by cascading multiple upsampling modules. In addition， the improved hybrid loss function was used to alleviate the common problem of sample imbalance in pathological images effectively. Experimental results on MoNuSeg2020 public dataset show that the improved segmentation method is 2.66 percentage points and 2.77 percentage points higher than U-Net in terms of F1-score and Intersection over Union （IoU） indicators， respectively， and effectively improves the pathological image segmentation effect and the reliability of clinical diagnosis.