A lesion of the sacroiliac joint is one of the primary signs for the early warning of ankylosing spondylitis. Accurate and efficient automatic segmentation of the sacroiliac joint is crucial for assisting doctors in clinical diagnosis and treatment. The limitations in feature extraction in sacroiliac joint CT images, due to diverse gray levels, complex backgrounds, and volume effects resulting from the narrow sacroiliac joint gap, hinder the improvement of segmentation accuracy. To address these problems, this study proposes the first U-shaped network for sacroiliac joint segmentation diagnosis, utilizing the concept of hierarchical cascade compensation for downsampling information loss and parallel attention preservation of cross-dimensional information features. Moreover, to enhance the efficiency of clinical diagnosis, the traditional convolutions in the U-shaped network are replaced with efficient partial convolution blocks. The experiment, conducted on a sacroiliac joint CT dataset provided by Shanxi Bethune Hospital, validates the effectiveness of the proposed network in balancing segmentation accuracy and efficiency. The network achieves a DICE value of 91.52% and an IoU of 84.41%. The results indicate that the improved U-shaped segmentation network effectively enhances the accuracy of sacroiliac joint segmentation and reduces the workload of medical professionals.