What's one approach you've taken to quantify uncertainty in your predictions, and how was it received?

Well, typically we tend to use Bayesian inferences or evaluation metrices to combat uncertainty but my personal favorite are Ensemble Methods i.e. training multiple models with different architectures or hyperparameters and combining their predictions. Most of the times, this "wisdom of the crowds" approach reduces overall uncertainty compared to a single model.