In supervised learning problems, it is common to have a lot of unlabeled data, but little labeled data. It is then desirable to leverage the unlabeled data to improve the learning procedure. One way to do this is to have a model predict "pseudolabels" for the unlabeled data, so as to use them for learning. In self-learning, the pseudo-labels are provided by the very same model to which they are fed. As these pseudo-labels are by nature uncertain and only partially reliable, it is then natural to model this uncertainty and take it into account in the learning process, if only to robustify the self-learning procedure. This paper describes such an approach, where we use Venn-Abers Predictors to produce calibrated credal labels so as to quantify the pseudo-labeling uncertainty. These labels are then included in the learning process by optimizing an adapted loss. Experiments show that taking into account pseudo-label uncertainty both robustifies the self-learning procedure and allows it to converge faster in general.