Given　the　diversity　of　medical　images,　traditional　image　segmentation　models　face　the　issue　of　domain　shift.　Unsupervised　domain　adaptation　(UDA)　methods　have　emerged　as　a　pivotal　strategy　for　cross　modality　analysis.　These　methods　typically　utilize　generative　adversarial　networks　(GANs)　for　both　image-level　and　feature-level　domain　adaptation　through　the　transformation　and　reconstruction　of　images,　assuming　the　features　between　domains　are　well-aligned.　However,　this　assumption　falters　with　significant　gaps　between　different　medical　image　modalities,　such　as　MRI　and　CT.　These　gaps　hinder　the　effective　training　of　segmentation　networks　with　cross-modality　images　and　can　lead　to　misleading　training　guidance　and　instability.　To　address　these　challenges,　this　paper　introduces　a　novel　approach　comprising　a　cross-modality　feature　alignment　sub-network　and　a　cross　pseudo　supervised　dual-stream　segmentation　sub-network.　These　components　work　together　to　bridge　domain　discrepancies　more　effectively　and　ensure　a　stable　training　environment.　The　feature　alignment　sub-network　is　designed　for　the　bidirectional　alignment　of　features　between　the　source　and　target　domains,　incorporating　a　self-attention　module　to　aid　in　learning　structurally　consistent　and　relevant　information.　The　segmentation　sub-network　leverages　an　enhanced　cross-pseudo-supervised　loss　to　harmonize　the　output　of　the　two　segmentation　networks,　assessing　pseudo-distances　between　domains　to　improve　the　pseudo-label　quality　and　thus　enhancing　the　overall　learning　efficiency　of　the　framework.　This　method＇s　success　is　demonstrated　by　notable　advancements　in　segmentation　precision　across　target　domains　for　abdomen　and　brain　tasks.