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Understanding model uncertainty is important for many applications. Wepropose Bootstrap Your Own Variance (BYOV), combining Bootstrap Your Own Latent(BYOL), a negative-free Self-Supervised Learning (SSL) algorithm, with Bayes byBackprop (BBB), a Bayesian method for estimating model posteriors. We find thatthe learned predictive std of BYOV vs. a supervised BBB model is well capturedby a Gaussian distribution, providing preliminary evidence that the learnedparameter posterior is useful for label free uncertainty estimation. BYOVimproves upon the deterministic BYOL baseline (+2.83% test ECE, +1.03% testBrier) and presents better calibration and reliability when tested with variousaugmentations (eg: +2.4% test ECE, +1.2% test Brier for Salt & Pepper noise).