Probability for label correctness in semi-supervised learning
I do not know any papers, I would be greatly appreciated if someone would link some. 

In my case, I always test by un-labelling my already known labelled Data by using a "traditional" 67% - 33% (train - test) split and checking how the labelling performs in various metrics (Accuracy, Logloss, etc.).

Moreover, there are various categories of Semi-Supervised Learning. For instance, if you would use Active Learning (pool based approach, includes incrementally sample/s each time), you could view how the performance ranges.

Finally, you may use as well Cross-Validation in the same sense for hyper parameter tuning for your Semi-Supervised Learning algorithm. All in all, in my perspective Semi-Supervised Learning, has a lot yet to offer and your question may be rather more domain, data specific and visualsing the Data and Clustering may give you new insights.