Bayesian inference, a statistical methodology rooted in Bayes’ theorem, offers the ability to compute probability distributions of unobserved phenomena, given observed information. To this end, this technique has proven useful in disease diagnosis based on equipment measurement. This paper proposes an innovative Bayesian inference strategy capable of rapidly estimating capillary oxygen supply capability in muscle tissues by leveraging uncertainty quantification techniques. Specifically, the oxygen supply capability is formulated with Krogh Erlang’s equation along with Fick’s second law. Moreover, the prior distribution of the early-time capillary oxygen supply capability is updated using acquired measurements of oxygen concentration within capillary to yield the posterior distribution. The resulting data with supportive simulation indicates that the cellular dimension can be efficiently updated, thereby facilitates the accurate uncertainty quantification of cellular environment estimate.