The　fast　and　robust　segmentation　of　tongue　images　is　a　prerequisite　to　achieve　automatic　tongue　diagnosis　in　traditional　Chinese　medicine.　In　order　to　assist　tongue　diagnosis　in　real-life　scenarios,　an　ideal　tongue　segmentation　method　would　need　to　obtain　the　entire　tongue　body　as　well　as　its　precise　contours.　However,　the　similar　appearance　among　the　tongue　body,　the　coating,　and　the　lips　hinders　the　performance　for　most　unsupervised　learning　methods　that　primarily　utilize　low-level　visual　features.　On　the　other　hand,　although　the　supervised　deep　convolutional　neural　networks　(DCNNs)　that　typically　depend　on　the　widely　used　crossentropy　loss　can　achieve　better　accuracy,　they　are　very　prone　to　segment　image　to　multiple　trivially　areas.　To　address　both　of　the　above　issues,　we　make　an　attempt　to　boost　the　segmentation　performance　of　DCNNs　with　a　novel　auxiliary　loss　function　that　seeks　to　exploit　large　margin　learning　for　end-to-end　tongue　segmentation　models.　Specifically,　we　first　propose　a　loss　function　that　involves　interclass　and　intraclass　costs　to　directly　measure　the　distance　among　pixels　that　belong　to　different　connected　regions.　Then,　we　explore　the　potential　ability　of　this　loss　function　as　a　regularization　for　different　segmentation　networks　such　as　those　with　attention　modules　and　the　deeply　supervised　network.　Finally,　a　theoretical　analysis　for　this　learning　scheme　is　presented.　Through　experiments　on　challenging　datasets,　we　show　that　the　proposed　approach　can　be　easily　integrated　into　state-of-the-art　networks　to　boost　their　performance　at　the　tongue　segmentation　task　without　bells　and　whistles.