Is there any way to artificially create a probability calibration for data coming from another model?

I have predictions, which come from a survival model, this model gives me very low probabilities, and I am not sure if they fulfill the real probability of the phenomenon.

For example, I calculate $P\left( T\leq t+d \middle| Tt \right)$ and the probabilities are very low (with $d=180$).

To summarize, I need these probabilities to be on average another number (let's say $0.2$). Is it possible to create an artificial calibration with only this number (the desired average) as the input?

I have thought of creating a vector of size $n$ equal to the size of that distributes $X_i \sim Ber(p=0.2)$ and assign its ones to the top $np$ probabilities and its zeros to the latest $n(1-p)$. Which would result in a table with a column of probabilities obtained with the survival model and another column with an $0$ or $1$ depending on the said probability.

After getting this table, I would simply use CalibratedClassifierCV from scikit-learn. Is this the correct way?