Unsupervised　domain　adaptation(UDA)　aims　to　mitigate　the　performance　drop　of　models　tested　on　the　target　domain,　due　to　the　domain　shift　from　the　target　to　sources.　Most　UDA　segmentation　methods　focus　on　the　scenario　of　solely　single　source　domain.　However,　in　practical　situations　data　with　gold　standard　could　be　available　from　multiple　sources　(domains),　and　the　multi-source　training　data　could　provide　more　information　for　knowledge　transfer.　How　to　utilize　them　to　achieve　better　domain　adaptation　yet　remains　to　be　further　explored.　This　work　investigates　multi-source　UDA　and　proposes　a　new　framework　for　medical　image　segmentation.　Firstly,　we　employ　a　multi-level　adversarial　learning　scheme　to　adapt　features　at　different　levels　between　each　of　the　source　domains　and　the　target,　to　improve　the　segmentation　performance.　Then,　we　propose　a　multi-model　consistency　loss　to　transfer　the　learned　multi-source　knowledge　to　the　target　domain　simultaneously.　Finally,　we　validated　the　proposed　framework　on　two　applications,　i.e.,　multi-modality　cardiac　segmentation　and　cross-modality　liver　segmentation.　The　results　showed　our　method　delivered　promising　performance　and　compared　favorably　to　state-of-the-art　approaches.