  Uncertainty quantification in a neural network is one of the most discussed
topics for safety-critical applications. Though Neural Networks (NNs) have
achieved state-of-the-art performance for many applications, they still provide
unreliable point predictions, which lack information about uncertainty
estimates. Among various methods to enable neural networks to estimate
uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short
period due to its simplicity. In this study, we present a new version of the
traditional dropout layer where we are able to fix the number of dropout
configurations. As such, each layer can take and apply the new dropout layer in
the MC method to quantify the uncertainty associated with NN predictions. We
conduct experiments on both toy and realistic datasets and compare the results
with the MC method using the traditional dropout layer. Performance analysis
utilizing uncertainty evaluation metrics corroborates that our dropout layer
offers better performance in most cases.
