The　inner　ear　labyrinth　is　a　combined　sensory　organ　of　hearing　and　balance,　which　is　surrounding　the　bony　cavity　located　in　the　petrous　temporal　bone.　The　structure　of　the　inner　ear　labyrinth　plays　an　important　role　in　otology　research　and　clinic　diagnosis　of　ear　diseases.　Automatic　and　accurate　segmentation　of　the　inner　ear　labyrinth　is　a　foundation　of　computer-aided　temporal　bone　quantitively　measurements　and　diagnosis.　The　inner　ear　labyrinth　is　characterized　by　its　complex　morphology,　small　size,　and　high　labeling　cost,　which　brings　challenges　for　deep　learning-based　automatic　segmentation　methods.　In　this　paper,　we　propose　a　robust　segmentation　method　for　the　labyrinth　in　temporal　bone　CT　images　via　multi-model　inconsistency.　In　the　active　learning　paradigm,　we　design　an　informative　sample　assessment　strategy　for　screening　informative　unlabeled　data.　An　observer　network　is　introduced　to　confirm　the　confidence　of　segmented　voxels　based　on　the　inconsistency　to　a　backbone　segmentation　network.　To　further　improve　the　efficiency　of　the　sample　screening,　a　maximum-connected　probability　map　(MCP-Map)　is　introduced　to　eliminate　the　influence　of　outliers　in　the　result　of　coarse　segmentation.　Experimental　results　show　that　our　methods　have　the　highest　labeling　efficiency　and　the　lowest　labeling　cost　compared　with　several　existing　active　learning　methods.　With　40%　labeled　reduce,　our　method　achieved　95.67%　in　Dice　Similarity　Coefficient　(DSC),　which　is　the　state-of-the-art　in　the　labyrinth　segmentation.