Why Orange "Predictions" and "Test & Score" produce different results on the same data?
Perhaps I can elaborate a bit on this.

Test&amp;Score is used for evaluating a model. You provide T&amp;S with training data and the learner (e.g. Random Forest) and then the widget performs 10-fold cross-validation on training data, leaving out 1/10 of a data for testing. Results of all 10 fold are then combined into a single evaluation result.

Predictions, however, does not perform cross-validation. It doesn't test the data provided. Instead, Random Forest passes a model to Prediction, not a Learner. A model is built on train data. Predictions widget does not consider the real class, it uses the model from RF and applies it to the (new) data.

These are two different procedures, thus you are getting different results.