Accurate segmentation of multiple organs based on computerized tomography (CT) images enables the precise diagnosis of lesions, facilitates rapid treatment planning, and improves the efficiency of clinical work. However, traditional segmentation algorithms often struggle with organs that have large deformations, small volumes, and blurry boundaries, resulting in relatively poor segmentation performance. This study proposes an improved U-Net medical image segmentation network called (MAU-Net), which aims to achieve accurate segmentation of multiple organs by introducing two modules. The multi-scale dilated convolution module captures multi-scale features of the target organs using different kernel sizes. The dynamic attention module precisely extracts important features to achieve weight balance between branches. The superiority of MAU-Net is confirmed through ablation experiments and comparative experiments with other mainstream networks. Compared to the traditional U-Net model, MAU-Net achieves an average Dice similarity coefficient (DSC) improvement of 3.39% and an average 95% Hausdorff distance (HD) reduction of 4.84 mm across all organs. MAU-Net demonstrates remarkable robustness and potential for applications in multi-organ segmentation tasks, contributing to improving clinical workflow efficiency and diagnostic accuracy in medical settings.