Are validation sets necessary for Random Forest Classifier? Is it necessary to have train, test and validation sets when using random forest classifier?

I understand it is important with Neural Networks but I am not understanding the importance of it with RF. I understand the idea of having a third unseen set of data to test on is important to know the model isn't overfitting, esp with Neural networks, but with RF it seems like you could almost not even have test or validation data (I know in practise this isn't true) but in theory since each tree of the forest   uses a random sample (with replacement) of the training dataset.

At the moment I am missing out on approx 250 samples by keeping them unseen from the train and test set and I know the model would improve with the extra data, so is it possible to have only train and test and not designate a seperate validation set, whilst still having a reliable model?
You can use Out-of-bag error as you validation error, if you are short of data.

As you may know, Random Forest fits multiple decision trees, and for each tree it only fits on a subset of data. So data that hasn't been used for fitting a given tree is called Out of Bag data, and it could be used as your validation set 1

Sklearn in Python has a hyperparameter of Out-of-bag error