# Interpreting the results of the probabilistic neural networks (Monte Carlo dropout approach)

I have a Keras NN model where I apply the Monte Carlo dropout approach as a predictive method to evaluate the uncertainty of the model outputs. From my research in the probabilistic neural networks, I understand that the lower the entropy (close to 0), the more probable that the predicted value is really certain.

In my model, I ran the inference step 100 times, averaged model output probabilities, and calculated the Entropy. The following is a randomly chosen instance from my results (mean_proba is averaged over 100 iterations of predict):

instance_id       y_true   mean_proba     Entropy
2301            1        0.9009       0.0940

My question is: from the instance# 2301 result, does this imply that the model has a low uncertainty, i.e., high information gain, that instance# 2301 should really be predicted as 1? Is this interpretation sound?