Can I include known uncertainty in a regression model? How can I build a regression model that accounts for the known error ranges (95% confidence intervals) in my independent variable samples?
I am not an expert in statistics, but I have the impression that there is still information in wide confidence interval samples but they should not be as important as samples with a narrow confidence interval.

This can be accomplished using measurement error models, errors in variables models, or latent variables models. I don't know if those are fundamentally different approaches or just different names for the same concept. These will help you estimate the effect of that unobserved "noiseless variable" if you observed only its noisy version. If your goal is to make a predictive model, these models might not be helpful since the predictions will be made only based on the noisy variable.
