Unsupervised　domain　adaptation　(UDA)　generally　learns　a　mapping　to　align　the　distribution　of　the　source　domain　and　target　domain.　The　learned　mapping　can　boost　the　performance　of　the　model　on　the　target　data,　of　which　the　labels　are　unavailable　for　model　training.　Previous　UDA　methods　mainly　focus　on　domain-invariant　features　(DIFs)　without　considering　the　domain-specific　features　(DSFs),　which　could　be　used　as　complementary　information　to　constrain　the　model.　In　this　work,　we　propose　a　new　UDA　framework　for　cross-modality　image　segmentation.　The　framework　first　disentangles　each　domain　into　the　DIFs　and　DSFs.　To　enhance　the　representation　of　DIFs,　self-attention　modules　are　used　in　the　encoder　which　allows　attention-driven,　long-range　dependency　modeling　for　image　generation　tasks.　Furthermore,　a　zero　loss　is　minimized　to　enforce　the　information　of　target　(source)　DSFs,　contained　in　the　source　(target)　images,　to　be　as　close　to　zero　as　possible.　These　features　are　then　iteratively　decoded　and　encoded　twice　to　maintain　the　consistency　of　the　anatomical　structure.　To　improve　the　quality　of　the　generated　images　and　segmentation　results,　several　discriminators　are　introduced　for　adversarial　learning.　Finally,　with　the　source　data　and　their　DIFs,　we　train　a　segmentation　network,　which　can　be　applicable　to　target　images.　We　validated　the　proposed　framework　for　cross-modality　cardiac　segmentation　using　two　public　datasets,　and　the　results　showed　our　method　delivered　promising　performance　and　compared　favorably　to　stateof-the-art　approaches　in　terms　of　segmentation　accuracies.　The　source　code　of　this　work　will　be　released　via　https://zmiclab.github.io/projects.html　,　once　this　manuscript　is　accepted　for　publication.　(c)　2021　Elsevier　B.V.　All　rights　reserved.