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Deep models are subject to performance drop when encountering appearance discrepancy, even on congeneric corpus in which objects share the similar structure but only differ slightly in appearance. This performance drop can be observed in automated ultrasound image segmentation. In this paper, we try to address this general problem with a novel online adversarial appearance conversion solution. Our contribution is three-fold. First, different from previous methods which utilize corpus-level training to model a fixed source-target appearance conversion in advance, we only need to model the source corpus and then we can efficiently convert each single testing image in the target corpus on-the-fly. Second, we propose a self-play training strategy to effectively pre-train all the adversarial modules in our framework to capture the appearance and structure distributions of source corpus. Third, we propose …