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Recent audio-visual voice activity detectors based on supervised learning require large amounts of labeled training data with manual mouth-region cropping in videos, and the performance is sensitive to a mismatch between the training and testing noise conditions. This paper introduces contrastive self-supervised learning for audio-visual voice activity detection as a possible solution to such problems. In addition, a novel self-supervised learning framework is proposed to improve overall training efficiency and testing performance on noise-corrupted datasets, as in real-world scenarios. This framework includes a branched audio encoder and a noise-tolerant loss function to cope with the uncertainty of speech and noise feature separation in a self-supervised manner. Experimental results, particularly under mismatched noise conditions, demonstrate the improved performance compared with a self-supervised learning baseline and a supervised learning framework.