B-cell epitopes play a key role in stimulating B-cells, triggering the primary immune response which results in antibody production as well as the establishment of long-term immunity in the form of memory cells . Consequently, being able to accurately predict appropriate linear B-cell epitope regions would pave the way for the development of new protein-based vaccines . Knowing how much confidence there is in a prediction is also essential for gaining clinicians' trust in the technology . In this article, we propose a calibrated uncertainty estimation in deep learning to approximate variational Bayesian inference using MC-DropWeights to predict epitope regions using the data from the immune epitope database . Having applied this onto SARS-CoV-2, it can more reliably predict B-cell epitopes than standard methods . This will be able to identify safe and effective vaccine candidates against Covid-19.