Propagating uncertainty through nested random forest models

Does anyone know if there are methods for propagating the prediction intervals (i.e. uncertainty) of nested surrogate models, specifically random forests? When I say nested, I mean that a second model is being trained from the predictions of an initial model. In other words, I am training an initial model from the raw data. This initial model can make predictions with uncertainty bounds. I then need to train a second model where the inputs are the predictions of the initial model. My question is how to properly represent the uncertainty of the predictions from the second model, given that the second model was trained on data that itself has uncertainty bounds. Any help on pointing me in the right direction with methods / keywords / papers would be appreciated.