Ankylosing spondylitis is a chronic inflammatory disease whose early diagnosis depends on the accurate identification of pathological features in the sacroiliac joint. However, due to the complex anatomical structure of the sacroiliac joint, the multiscale heterogeneity of lesions, as well as interference from partial volume effects and noise in CT imaging, the accuracy of traditional segmentation methods often fails to meet clinical demands. To address these challenges, this study proposes a Multiscale Attention-Guided U-Net (MAG-UNet). The model enhances local-global feature representation through a Multiscale Feature Fusion (MFF) module, integrates spatial-channel adaptive weighting via a Dual-path Attention (DA) mechanism, and introduces a Large-kernel Grouped Attention Gate (LGAG) to resolve cross-scale feature coupling issues. Experiments conducted on a dataset provided by Shanxi Bethune Hospital demonstrate that MAG-UNet achieves significant performance improvements in sacroiliac joint CT segmentation, with a Dice coefficient of 92.4% and an Intersection over Union (IoU) of 86.0%, surpassing the baseline U-Net model by 3.4% in IoU. This study provides a reliable technical solution for the early diagnosis of AS, offering substantial clinical value and broad potential for practical application.