In this work we investigate several issues in order to improve the per- formance of probabilistic estimation trees (PETs). First, we derive a new prob- ability smoothing that takes into account the class distributions of all the nodes from the root to each leaf. Secondly, we introduce or adapt some new splitting criteria aimed at improving probability estimates rather than improving classifi- cation accuracy, and compare them with other accuracy-aimed splitting criteria. Thirdly, we analyse the effect of pruning methods and we choose a cardinality- based pruning, which is able to significantly reduce the size of the trees without degrading the quality of the estimates. The quality of probability estimates of these three issues is evaluated by the 1-vs-1 multi-class extension of the Area Under the ROC Curve (AUC) measure, which is becoming widespread for evaluating probability estimators, ranking of predictions in particular.