# How to calculate the data noise variance for a prediction interval?

I have a neural network that connects $$N$$ input variables to $$M$$ output variables (qoi). By default, neural networks just give out point estimations.

Now, I want to plot some of the quantity of interests and produce also a prediction interval. To calculate the model uncertainty, I use the bootstrap method.

$$\sigma_{model}^2=\frac{1}{B-1}\sum_{i=1}^B(\hat{y}_i^b-\hat{y}_i)^2\qquad \text{with}\quad\hat{y}_i = \frac{1}{B}\sum_{b=1}^B\hat{y}_i^b$$ $$B$$ training datasets are resampled from original dataset with replacement. $$\hat{y}_i^b$$ is the preditcion of the $$i$$ sample generated by the $$b$$th bootstrap model.

If I understood it correctly, the model uncertainty (or epistemic uncertainty) is enough to create a confidence interval.

But for the PI I also need the irreducible error $$\sigma_{noise,\epsilon}^2$$. $$\sigma_y^2= \sigma_{model}^2+\sigma_{noise,\epsilon}^2$$

The aleatoric uncertainty is explained in the following picture: Is there a procedure to calculate this aleatoric uncertainty?

I read the paper High-Quality Prediction Intervals for Deep Learning and watched the corresponding YouTube video. And I read the paper Neural Network-Based Prediction Intervals.

EDIT I suggest the following algorithm to estimate the noise variance, but I am not sure if this makes sense: 