Gleason Score (GS) is the principal histological grading system to support the quantification of cancer aggressiveness. This analysis is carried out by expert pathologists but with reported evidence of moderate agreement among pathologists (kappa values less than 0.5). This fact can be prone to errors that directly affect the diagnosis and subsequent treatment. Current deep learning approaches have been proposed to support such visual pattern quantification but there exist a remarked on expert annotations that overfit representations. Besides, the supervised representation is limited to model the high reported visual variability intra Gleason grades. This work introduces a semi-Supervised Learning (SSL) approach that initially uses a reduced set of annotated visual patterns to built several GS deep representations. Then, the set of deep models automatically propagates annotations to unlabeled patches. The most confident predicted samples are used to retrain the ensemble deep representation. Over a patch-based framework with a total of 26259 samples, coded from 886 tissue microarrays, the proposed approach achieved remarkable results between grades three and four. Interestingly, the proposed SSL with only the 10% of samples achieves more general representation, achieving averages scores of ~75.93% and ∼ 71.88% concerning two expert pathologists