predicted labels or predicted probabilities in a ROC plot?
Use the predicted labels.
Explanation - The ROC curve show possible classification performance at different setups.
Where 'classification performance' is a factor of True-positive-rate and False-positive-rate. And 'different setups' are thresholds used to determine a predicted label(class) based on predicted probability.
The latter is the reason why you should use predicted labels and not predicted probability. 

Some additional context:
In my experience, ROC curve can be useful for three things:
1) Determining optimal setup(probability threshold) for a given model. Based on possible setups and problem needs.
2) Evaluating overall model performance and different models comparison. This can be achieved using the AUC.
3) Understanding classification model behavior and identifying possible abnormalities. For example, if the curve has a step like pattern its possible your model is too simple.  

Here is a short nice explanation on the ROC curve.