Validation is the main bottleneck preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some objective metrics. In this work, a different approach based on Petri Nets (PN) is proposed. The basic idea consists in predicting the accuracy that will result from a given processing based on the characterization of the sources of inaccuracy of the system. Here we propose a proof of concept in the scenario of a diffusion imaging analysis pipeline. A PN is built after the detection of the possible sources of inaccuracy. By integrating the first qualitative insights based on the PN with quantitative measures, it is possible to optimize the PN itself, to predict the inaccuracy of the system in a different setting. Results show that the proposed model provides a good prediction performance and suggests the optimal processing approach.